Individual Tree

Individual tree analysis focuses on identifying, characterizing, and quantifying individual trees within a forest or urban environment using various data sources, primarily aiming to improve forest management and ecological monitoring. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs) and transformer architectures, often coupled with lidar point cloud data and high-resolution imagery, to achieve accurate tree segmentation and parameter estimation (e.g., diameter at breast height, tree height). These advancements enable automated forest inventories, improved assessments of forest health and carbon sequestration, and more efficient urban forestry planning, impacting both ecological research and practical applications.